arXiv:1706.02766
Evolutionary Multitasking for Multiobjective Continuous Optimization: Benchmark Problems, Performance Metrics and Baseline Results
원문 초록
In this report, we suggest nine test problems for multi-task multi-objective optimization (MTMOO), each of which consists of two multiobjective optimization tasks that need to be solved simultaneously. The relationship between tasks varies between different test problems, which would be helpful to have a comprehensive evaluation of the MO-MFO algorithms. It is expected that the proposed test problems will germinate progress the field of the MTMOO research.
함께 살펴볼 논문
공통 태그를 근거로 연결했습니다. 실제 인용 관계나 논문의 우수성을 뜻하지 않습니다.
Bridging Evolutionary Multiobjective Optimization and GPU Acceleration via Tensorization
Principal Orthogonal Latent Components Analysis (POLCA Net)
LibMOON: A Gradient-based MultiObjective OptimizatioN Library in PyTorch
SustainDC: Benchmarking for Sustainable Data Center Control
GPU-accelerated Evolutionary Multiobjective Optimization Using Tensorized RVEA
Explainable Bayesian Optimization
토론
이 논문과 연결된 포럼 글이 아직 없습니다.